KR101738898B1

KR101738898B1 - Market forecasts providing method and computer program thereof

Info

Publication number: KR101738898B1
Application number: KR1020150166105A
Authority: KR
Inventors: 김상훈; 김승민; 이상현; 이철웅; 임현우; 윤점열; 김용태; 이철흠
Original assignee: 사단법인 한국신용정보원
Priority date: 2015-09-03
Filing date: 2015-11-26
Publication date: 2017-06-08
Also published as: KR20170028231A

Abstract

The present invention relates to a method and a program for providing market forecast information. More specifically, the present invention relates to a method and a system for providing market forecast information for a predetermined item by using statistical office item data for each user, How to provide that program.
A method for providing market forecast information according to an exemplary embodiment of the present invention includes the steps of: selecting a market forecast menu by accessing a market forecast information providing server from a user terminal, selecting an item to receive market forecast information, a saturating market scale, A step of inputting a market attractiveness (qualitative) at the present time; a step of tying a total growth model model of a corresponding item on the basis of data input from a market prediction module of the market forecast information providing server; The method of claim 1, further comprising: combining a time series data base trend and a recent time series data base trend and applying the same to the growth model to derive a forecast curve of the market and calculating a market size; And providing market forecast information including a forecast (growth) curve and a market size.

Description

[0001] 1. Field of the Invention [0002] The present invention relates to a market forecast providing method and a computer program,

The present invention relates to a method of providing start forecast information and a computer program thereof. More particularly, the present invention relates to a method for providing start forecast information and a computer program thereof, And to a computer program thereof.

A modern economy can be defined as a knowledge-based economy, and a knowledge-based economy means an economy in which intangible assets such as technology, knowledge, and information become the key infrastructure factor of wealth creation and employment.

As the knowledge-based economy becomes more and more widespread, the financial system, which becomes the core of the economy, is required to be transformed from the old paradigm of large corporations and mortgages, to a technology finance system capable of supporting innovative technologies and ideas based on future values.

In order to revitalize technology finance, it is necessary to establish a virtuous cycle ecosystem centered on technology evaluation system that can evaluate technology and ideas properly. Reliable and timely technology and market information for technology evaluation should be established in the basis of this ecosystem.

R & D and commercialization of technological achievements tend to depend on market needs and attributes. Therefore, estimating future market size and growth potential is very important factor in evaluating technology value and technology success.

Especially, as the intensifying competition between companies and accelerating the emergence of alternative and competitive technologies, the technology life cycle is shortened and the need for market trends and prediction is increasing more and more.

Conventionally, the method of predicting market size and growth potential can be divided into qualitative method and quantitative method. Survey method and scenario method such as Delphi expert are mainly used as qualitative methods. However, Costs can be high, and limitations and consistency problems can arise in sample research. There is a limit to quantitatively measuring and forecasting the market.

As a quantitative method, a method of extrapolating past values is mainly used. Practically, a compound annual growth rate (CAGR), which simply extrapolates recent values, is mainly used.

The CAGR has a merit that can easily predict future market growth, but it does not reflect the specificity of the technology and the dynamics of the market, and may cause estimation distortion due to recent events.

As a quantitative market forecasting method that extrapolates past values in addition to the above CAGR, a method using a conventional growth model (Logistic, Bass, Gompertz, etc.) is attempted, but a product limited to a specific industry having the same property or reflecting a detailed technical characteristic , And it does not reflect the volatility due to changes in the external environment, resulting in less practical use.

On the other hand, the prior art publication No. 10-2014-0053444 discloses a storage medium for storing a market size predicting device, a market size predicting method, and a program for predicting a market scale.

The dual market size prediction method includes receiving technology or product keywords from a user;

The method comprising the steps of: receiving bibliographic information including search traffic information, detecting whether there is a hype cycle in the search traffic information for the received keyword, detecting, when the hiphycle cycle is detected, Calculating a maximum market size, generating a prediction model using the calculated maximum market scale, calculating a first future market scale using the generated prediction model, or, if the hip cycle is not detected, Calculating a second future market size by using a regression analysis model of search traffic information and a sales rate by time of a consumer selection attribute keyword as a keyword; And

Providing information on any one of the calculated first future market size or the calculated second future market size to a user; .

However, since this precedent patent also provides the market size through the growth model (forecasting model), it is not limited to the specific industry with the same property or reflects the detailed technical characteristics, And thus the practical utilization is reduced.

Publication No. 10-2014-0053444 (Published date May 08, 2014)

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above-described problems, and provides market prediction information that can provide information on market size and market growth prediction based on the technical characteristics of the product unit and the volatility of the external environment based on the growth model Method and program thereof.

Another object of the present invention is to provide a method and a program for providing market forecast information that can be universally and practically used throughout the entire industry.

Another object of the present invention is to provide a market prediction information which can support the technical innovation of SMEs and promote technology commercialization by using market information on market size and growth potential in accordance with various purposes such as technology evaluation, Method and program thereof.

According to another aspect of the present invention, there is provided a method for providing market forecast information, comprising the steps of: (a) selecting a market forecast menu by accessing a market forecast information providing server from a user terminal, , Entering the saturation market size, market cycle, current period of market cycle, and market attractiveness (qualitative);

(b) typing an overall growth model model of the item based on the data input by the market prediction module of the market prediction information providing server;

(c) combining the long-term time series data base trend and the recent time series data base trend in the market prediction module and applying the same to the growth model model to derive a forecast (growth) curve of the market and calculating a market scale; And

(d) providing market prediction information including a market forecast (growth) curve of a business type and a market size to the user terminal in the market prediction module; To

.

The market forecast information providing program according to the embodiment of the present invention is a market forecast information providing program for providing market forecast information of an item to a user terminal,

Receiving a market forecast information from the user terminal, a saturation market scale, a market period, a current market point among market cycles, and a market attractiveness (qualitative)

Typing an overall growth model model of the item based on the input data;

Combining the long-term time-series data base trend of the item with the recent time series data base trend and applying the same to the growth model model to derive a forecast (growth) curve of the market and predicting the market scale;

And providing market forecast information including a market forecast (growth) curve of the item and a market size to the user terminal.

According to the solution of the above-mentioned problems, the information on the market size and the growth potential of the market can be provided by reflecting the technical characteristics of the product unit and the volatility of the external environment based on the growth model.

In addition, market forecasting information can be used universally and practically throughout the entire industry.

In addition, market information on future market size and growth potential can be used for various purposes such as technology evaluation, technology finance, and investment to support SMEs' technological innovation and promote technology commercialization.

1 is a block diagram of a system for implementing a method of providing market forecast information according to an embodiment of the present invention.
2 is a flowchart illustrating a method of providing market forecast information according to an exemplary embodiment of the present invention.
3 is a detailed flowchart of the item input step shown in FIG.
4 is a detailed flowchart of the step of calculating the market attractiveness shown in Fig.
5A and 5B are diagrams for explaining the flow chart of FIG.
Figs. 6A and 6B are diagrams illustrating output examples of other screens for explaining the flowchart of Fig. 2. Fig.
7A and 7B are diagrams showing a technology market prediction model for facilitating understanding of the present invention.
8A to 8D are exemplary views of the steps shown in Fig.
Figs. 9A to 9E are exemplary diagrams for explaining each step shown in Fig.
FIG. 10 is a view for explaining the type classification step shown in FIG. 2. FIG.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

It is to be noted that the same components of the drawings are denoted by the same reference numerals and symbols as possible even if they are shown in different drawings.

In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Also, when a part is referred to as "including " an element, it does not exclude other elements unless specifically stated otherwise.

First, some of the various market growth or demand forecasting models will be described in order to understand the present invention.

First, the modified-exponential curve growth model predicts the market assuming that cumulative sales volume up to the present time (t) follows the exponential curve type (Y _t = a [1-exp (-bt)]

Second, the logistic growth model estimates market growth assuming that Y _t = L / [1 + exp (a-bt)], the cumulative function of the normal distribution.

Third, the growth model of Bass is a combination of exponential curve growth model and logistic growth model considering so - called 'innovation effect' and 'imitation effect'.

Fourth, the Gompertz growth model is basically similar to the logistic growth model, but it has an asymmetric S shape instead of symmetrical shape centering on the inflection point.

Fifth, the Weibull growth model shows various probability distributions by generalizing the exponential distribution, and it is often used to predict the life of a product or a part.

The growth model used in the present invention among the above growth models will be described in detail. The cumulative sales volume Y _t from the logistic growth curve curve shown in FIG. 7A to the present time _t is expressed by Equation (1).

Where L is the saturation market size (potential market), α _F is the location parameter in FP (Fisher-Pry), α _P is the location parameter in PR (Pearl-Reed), β is the shape parameter or growth parameter

)to be.

The logistic function can be variously represented by Mansfield-Blackman, PR, FP, etc. according to the function expression method. This is because there are advantages and disadvantages in ease of processing the variable according to each function. In the growth model of the present invention, Since the FP positional parameters represent an average, the calculation was based on the Mansfield-Blackman function and the FP function.

The cumulative observation value Y _t from the bath growth model curve shown in FIG. 7B to the present time _t is represented by Equation (2).

Where S (t) is the observed value at the present time, L is the saturation market size, p is the innovation factor (initial purchase probability), and q is the imitation coefficient.

The innovation coefficient (p) is an external influence coefficient, which is an independent influence factor for users of existing products. The imitation coefficient (q) is an internal influence coefficient, which is a coefficient indicating the influence of existing product users on the next adopter.

1 is a block diagram of a system for implementing a method of providing market forecast information according to an embodiment of the present invention.

As shown in the figure, the user terminal 10, the market prediction information providing server 20, the market prediction information providing server 20, and the data server 30 are connected through a wired / wireless communication network.

The user accesses the market prediction information providing server 20 through the wired / wireless communication network by using the user terminal 10 to select the market prediction menu and selects the item I, the saturation market size L, the entire market period T, (T) and market attractiveness (qualitative) in the entire market cycle (see Fig. 5A).

In addition to this, it is possible to further input the amount of production (market size, shipment amount) in the recent five years (see FIG. 6A).

The user can be divided into an external expert and an internal expert.

At this time, if it is an external non-specialist, it shall be possible to input it by choice, except for the production amount (market size, shipment amount) for the recent 5 years, and the item (I) can input the similar item which is not defined in the statistical office .

If you are an internal expert, you can make use of both the selection and direct input, and the item will be able to input the items specified in the National Statistical Office.

The market prediction module 24 and the market attractiveness calculation module 26 of the market forecast information providing server 20 process the information input by the user and the data stored in the raw material DB 52 and output the processed data to the user terminal 10 Market forecast information (figures and graphs, market attractiveness (quantitative, qualitative)).

At this time, if the user is an internal expert, the market prediction information as shown in FIG. 5B is provided. If the user is an external expert, the market prediction information as shown in FIG. 6B is provided differently.

If the external expert or the internal expert does not know the item to be provided with the market forecast information, the user terminal 10 accesses the market forecast information providing server 20 through the wired / wireless communication network, selects the association menu, Enter the name.

The code linkage module 22 of the market forecast information providing server 20 can process the code data and provide a code-specific association to the user terminal 10 (code-linked service) .

The data server 30 provides data required by the market prediction information providing server 20, for example, data or code data for each type of business, major macro / market / technology related indexes, and the like, 20) in the raw material DB 52. [0054]

The data server 30 may be a server such as the statistical office 31, the bank 32, the patent office 33, and the expert group 34, for example.

The market forecast information providing server 20 periodically collects and processes various data (data stored in the raw material DB, information input by the user, data provided by the data server, etc.) for the user, predicts the market, And stores the market forecast information in the machining information DB 54. [

2 is a flowchart illustrating a method of providing market forecast information according to an exemplary embodiment of the present invention.

First, the user terminal 10 accesses the market prediction information providing server 20 through a wired / wireless communication network to select a market prediction menu (S20). Then, as shown in FIG. 5A, (L), the total market cycle (T) of the item, the current point (t) of the entire market cycle, and the market attractiveness (qualitative) of the item are inputted (S21).

Herein, the input of the item (I) may be associated with the code link service according to the flow chart of FIG. 3, or may be directly input or selected.

The input of the saturating market size (L) may provide a basic saturating market size and may be selected by a multiple of the basic saturating market size or by direct input.

The market attractiveness includes the market conditions that are difficult to quantitatively judge. The KPI (Key Performance Index) of the competitive factors is classified into the competitiveness factor and the environmental factor by VD (Value Driver) The KPI of environmental factors is divided into A (the highest grade) and the E grade (the lowest grade), and it is divided into the items of the market structure (competition situation) and the constraint factors such as the legal regulations.

In addition to this, the innovativeness of the item, the growth of the item (β), and the upper industry classification trend of the item can be further input.

This innovation is judged to be innovative when p> q using innovation coefficient (p) and imitation coefficient (q).

The growth rate β is a parameter indicating a slope of a curve in the graph of the market prediction information, which is closely related to the standard deviation as a shape parameter or a growth parameter.

The upper industrial classification is input so that the sum of a, b, and c is 1 using the classification (a), the middle classification (b), the classification (c)

In addition, it is possible to further input the amount of production (market size, shipment amount) of the recent five years (see FIG. 6A).

Next, the market prediction module 24 of the market prediction information providing server 20 types the global growth model model of the corresponding item using the data of the raw material DB 52 and the information input by the user (S22).

To this end, the market prediction module 24 collects the middle class or small class industry data corresponding to the input item.

In other words, data of the sub-category or sub-category industry is collected by using the GDP data of the Bank of Korea and the output or sales data of the National Statistical Office.

As shown in FIG. 10, the method of typing is performed through the following four steps.

In the first step (standard value calculation), the data for all industrial categories collected above are standardized based on the initial year value.

In the second step (creation of the relative value graph), the graph of the relative value of the industry by year is drawn and the growth characteristics of the market size of the relevant industry are grasped.

In the third step (relative value calculation), the standard value of the subclass is divided by the standard value of the corresponding upper class, and the relative size is calculated from the upper class of each business type.

In the fourth step (type classification), the classification is again made at each industrial classification level according to the given type classification standard.

At this time, sequential type classification is performed with the classification level, middle classification level, and small classification level to derive the market size growth type of each industrial classification.

The type classification at the above-mentioned classification level provides a macroscopic level based on the growth of the whole country, and a more detailed type can be derived when the classification is performed up to the level of the small classification.

In order to reflect the characteristics of the technology market, the next market prediction module (24) calculates a prediction curve of the technology market (S23) by considering the past macroeconomic and industrial economic trends and recent related industry trends in a comprehensive manner.

In other words, the growth curve of the technology market is calculated by combining the long-term time-series data base trend of the relevant industry with the recent time series data base trend and applying the same to the above-mentioned prediction model.

In this case, the size of the packaging market (L) is set considering the observation time (Tt) and various other factors. For the recent trends, the data of the upper industrial classification (subdivided or subdivided) L is derived by combining the above logistic and linear regression considering the upper industrial classification.

In addition to this, based on the existing market size of the item and industry and the growth curve, we derive the numerical value at the future specific time point and calculate the predicted market size.

At this time, it reflects the expert's score on the detailed item evaluation index and reflects the external environment variable.

Also, in step S23, the market attractiveness calculating module 26 quantitatively and qualitatively calculates market attractiveness, and evaluates the market attractiveness in terms of four types as short and long term.

Step S23 of the market attractiveness calculation module 26 will be described in detail with reference to FIG. 4, which will be described later.

The market prediction module 24 provides the user terminal 10 with the market prediction information including the market scale prediction (growth) curve or the market scale prediction value of the input item as shown in FIG. 5B or 6B S24).

At this time, the market prediction module 24 provides the quantitative index and the qualitative index of the market attractiveness.

As shown in FIG. 5B, in the case of an internal expert, the market size prediction graph and the market size prediction value all show seven items including CAGR (combined average annual growth rate).

Here, the graph from the market scale 1_L to the market scale 3_L is the graph and the numerical value predicted through the logistic function (growth model) of FIG. 7A, and the graph predicted through the Bass function (growth model) of FIG. It is a figure.

In addition, market size of 1_L and market size of 4_B are predicated on recent data, market size of 2_L and market size of 5_B predicting expert input data, market size of 3_L and market size of 6_B predicting long term trends.

At this time, the market size can be given to the internal experts by showing the previous five years and the following five years based on the present.

Also, as shown in FIG. 6B, in the case of an external non-expert, the market size prediction graph and the market size prediction value indicate the market size 2_L and the market size 5_B.

The market size 2_L is a graph and numerical value predicting professional input data using the logistic function (growth model) of FIG. 7A. The market size 5_B is a graph predicting expert input data using a Bass function (growth model) And figures.

In the step S21, when the innovator is inputted, when the external non-expert is inputted, only the market size of 5_B is displayed, and when it is inputted as normal, only the market size is displayed as 2_L.

At this time, the market size can be given to external specialists by indicating the previous five years and the following five years based on the present.

FIG. 3 is a detailed flowchart of the item input step shown in FIG. 2, and FIGS. 8A to 8D are exemplary diagrams of the steps shown in FIG.

First, the code linking module 22 receives various code data from the data server 30 and associates it with an industry technical classification code, a standard industry classification code, an item code, and an IPC code, The industrial technical classification code and the IPC code are associated with each other and stored in the original sheet through a spreadsheet program such as Excel (S40).

In this state, if the external expert or the internal expert does not know exactly the item for which the market forecast information is to be provided, the user terminal 10 accesses the market forecast information providing server 20 through the wired / wireless communication network to select the association degree menu (S41) A code or a name is input (S42).

Next, the code linking module 22 of the market prediction information providing server 20 constructs only the necessary data among the data of the original sheet and stores it as a first preparation procedure sheet (S43).

The necessary data is composed of a standard industrial classification code (subcategory classification), an item code, an industrial technical classification code (up / down), and an IPC code (up / down).

At this time, the subcategories of the standard industrial classification are composed of 6 digits codes, the item codes are composed of 9 digits codes, the industrial technical classification codes are composed of 6 digits, and the IPC codes are composed of 4 digits.

Here, it is classified according to the degree of association between the upper and lower codes.

FIG. 8A illustrates a first preparation procedure sheet of an industrial technical classification when an industrial technical classification code 400301 is searched. The associated standard industry classification, item code and association degree are outputted up / down, and the IPC code It can be seen that the upper and lower sections are divided and output.

Even in the case of the name search, the first preparation procedure sheet can be obtained in the same manner.

Next, the code linking module 22 sums the score (degree of association) related to the code linkage based on the result obtained in the first preparation procedure sheet, and stores it in the second preparation procedure sheet (S44).

Here, the score related to the code linkage is a value indicating how much the association between the industrial technology classification code, the standard industry classification code, the item code, and the IPC code is related to the previous research, existing mapping data, and expert evaluation.

That is, the score related to the code linkage is a weighted value obtained by dividing the degree of association between codes up / down.

More specifically, the industrial technology classification and the IPC associated with each item are divided into upper and lower parts, and the industrial technology classification-IPC linkage is divided into four parts of imagination, upper, lower, lower, and lower, The weight α (α> 1) is assigned to the output value and the weight β (1> β> 0) is assigned to the output value.

Accordingly, weights are calculated for each degree of importance, and the weights are added for each code.

When outputting the results, weights can be expressed by standardization on the basis of 100 points.

FIG. 8B illustrates a second preparation procedure sheet of an industrial technical classification item when the industrial technical classification code 400301 is searched. When the result obtained by adding scores for each item code, each IPC code, and each standard industry classification code is an item The IPC score, and the standard industry score.

Even in the case of the name search, the second preparation procedure sheet can be obtained in the same manner.

Next, in the code linking module 22, the result obtained in the second preparation procedure sheet is arranged to ultimately appear on the search sheet, and is stored as a result sheet (S45).

FIG. 8C illustrates a result sheet of an industrial technical classification item when the industrial technical classification code 400301 is searched. The result shows a score corresponding to a standard industry classification code, an item code, and an IPC code. Rank) is given.

Even in the case of the name search, the result sheet can be obtained in the same manner.

Next, in the code linking module 22, the degree of association between each code is standardized on the basis of 100 points, and finally, the code is displayed in a search result display field of the search sheet and provided to the user terminal 10 (S46).

FIG. 8D illustrates a result of retrieving the industrial technology classification code 400301, and it can be seen that the standard industry classification, the item, the IPC search result, the code, name, and the standardized association degree for each item are shown.

Then, the user selects (determines) the corresponding item by viewing the degree of association between codes output to the user terminal 10 (S47).

FIG. 4 is a detailed flowchart of a step of calculating the market attractiveness shown in FIG. 2, and FIGS. 9A to 9E are exemplary diagrams for explaining each step shown in FIG.

First, the market attractiveness calculating module 26 of the market forecast information providing server 20 collects data for each category of the item from the raw material DB 52 in order to quantitatively calculate the market attractiveness (S50) Collect relevant data, collect data on the minimum classification level for manufacturing industries, and data for intermediate classification levels for service industries.

Next, the market attractiveness calculating module 26 reflects the specificity of each type of business by analyzing long-term and short-term factors for each type of business (S51).

At this time, the major variables for reflecting the specificity of each industry are basically manufacturing value, and service industry focuses on sales change.

In addition, employment growth rate, wage change rate, price change rate, and interest rate change rate are the main variables.

The rate of change of each major variable is calculated by the following equation (3).

Where X _{i, t} is the rate of change of the main variable (Y _i ) at time t.

In addition, long-term data are used for annual data and short-term data are used for monthly and quarterly data. Annual data on which monthly and quarterly data are accumulated are reflected in long-term trends. Monthly and quarterly data are reflected in short-term economic fluctuations.

For example, as shown in FIG. 9a, the influence of major variables on the overall manufacturing industry is strongly influenced by changes in employment (+), interest rate (-) and price (+) in the long term, Has a limited effect.

In the long term, the interest rate (-) has a strong influence on the overall service industry. In the short term, the change in employment (+) has a strong influence.

Next, the market attractiveness calculating module 26 quantitatively calculates the attractiveness of the market by reflecting the short-term and long-term evaluations of each industry in consideration of the main variables (S52).

More specifically, the growth rate F is standardized according to the following equation (4) using the rate of change of the variables of Equation (3).

In this case, F _j ^t is the standardized growth forecast for each time (t) of j industry _, and it is compared with the average growth level during past period (T) Is normalized by the standard deviation (? _Xj ) for each variable and is then calculated as the sum of these.

In the case of the standardized growth forecasts according to the long-term viewpoint in Equation (4), whether or not the standardized growth forecasts between the past 1, 3, and 5 years (t = 1, 3, 5) If the value is positive, the industry is likely to increase (grow) in the future as it shows a steady increase (growth) trend.

In the equation (4), the standardized growth forecast for each short-term viewpoint shows whether the standardized growth forecast between the last 2, 4, and 6 quarters (t = 2, 4, 6) .

Through the above procedure, the estimates derived for each period are standardized to have values of 0 to 100 using Equation (5).

That is, V _j ^t is a value obtained by normalizing the growth forecast (F _j ^t ) for each time point ^t from 0 to 100.

The long-term and short-term attractiveness of the market is quantified by Equations (6) and (7) by weighted averaging of the standardized forecasts by applying a high weight to recent estimates.

The two mai of Equation (6) and Equation (7) are the attractiveness of the long term and the short term, respectively, and ω is the weight by time, It is derived every 6 quarters.

At this time, the more attractive the market attractiveness is to the industry (industry), the more likely it is to grow.

In order to analyze the contribution of the market attractiveness to the growth rate of each industry through the following Equation (8), the relative level of market attractiveness relative to the industry (manufacturing or service industry) belonging to each industry (j) Or service industry), we can quantify the influence of the industry on growth in the future.

For example, if standardized growth forecasts are shown for each code as shown in FIG. 9B, the C101 code has grown 8.34% over the past 10 years, but has been growing for 5, 3, and 1 years in recent years. And the market attractiveness is low at 17.63.

This low market attractiveness is expected to reduce the growth rate of the C101 code sector by about 1.88% p.

In addition, the C103 code is expected to grow in the future as the standardized growth forecasts for the past 5, 3, and 1 years have increased to 0.24, 0.29, and 0.90, respectively. The sector's growth rate is expected to increase by 2.02% p.

If we compare the C103 code with the C112 code based on the market attractiveness, we believe the growth will continue in the long term. However, we expect the C112 code to grow more strongly in the future.

Next, the market attractiveness calculation module 26 classifies the short and long term growth types of each industry (industry) into four types (PP, PN, NP, NN) using standardized growth forecasts and market attractiveness, (S53).

The PP type is highly attractive to the market due to its long-term growth trend, and is attractive to the market thanks to sufficient demand in the short term.

Although the PN type has a high market appeal due to long-term growth trend, it seems to have a low market attractiveness due to lack of short-term demand due to economic factors.

The NP type is attractive to the market due to long-term downward trend, but it is possible to secure short-term demand due to economic factors.

The NN type has a low market attractiveness due to a downward trend in the long term and a low market attractiveness due to lack of short-term demand.

For example, if the long-term growth prospects and market attractiveness are derived as shown in Figure 9c in the basic chemical manufacturing industry, the long-term outlook is likely to reflect the global petrochemicals decline in 2011, ), And it has been shifting to positive growth in favor of the recent one year, but it is only 0.16 times the standard deviation of the manufacturing industry.

From a short-term perspective, we see short-term shortage of demand due to economic factors, persistent demand shortage, and rapid deterioration rate.

As a result, the industry is considered as a PN-type industry with long-term market appeal, but current technology is mature, and short-term unstable factors are expected to lead to a contraction in supply.

In the light bulb and lighting manufacturing sector, if the long-term growth prospects and market attractiveness are derived as shown in Fig. 9d, the LED business, which is a representative business part, is continuously growing (production, sales, and employment) Year-on-year growth forecast is negative (-) due to deterioration in operating profit at major domestic companies.

From a short-term point of view, we believe that the short-term outlook is negative due to continued deterioration in operating profit from 2012 onward.

As a result, the industry is regarded as a PN-type industry with long-term market attractiveness. Currently, it is the growth period of technology and there is a short-term unstable factor.

The market attractiveness calculating module 26 provides the quantitative index and the qualitative index of the market attractiveness to the user terminal 10 as shown in FIGS. 5B and 6B (S54).

In this case, as shown in FIG. 9E, the qualitative index of the market attractiveness is divided into the competitive factors and the environmental factors by evaluating the market attractiveness inputted by the user, and the competitive factors are matrixed by the product (service) , And environmental factors are matched by market structure (competition situation) and regulatory factors, and then they are positive, indicating the qualitative index of overall market attractiveness.

Although the preferred embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.

In addition, it is a matter of course that various modifications and variations are possible without departing from the scope of the technical idea of the present invention by anyone having ordinary skill in the art.

10: user terminal 20: market forecast information providing server
22: code link module 24: market prediction module
26: Market attractiveness calculation module 30: Data server
52: raw material DB 54: processing information DB

Claims

(a) A user terminal is connected to a server for providing forecast information to select a market prediction menu, and input items such as a saturation market size, a market cycle, a current market point, and a market attractiveness (qualitative) step;
(b) typing an overall growth model model of the item based on the data input by the market prediction module of the market prediction information providing server;
(c) combining the long-term time series data base trend and the recent time series data base trend in the market prediction module and applying the same to the growth model model to derive a forecast (growth) curve of the market and calculating a market scale; And
(d) providing market prediction information including a market forecast (growth) curve of a business type and a market size to the user terminal in the market prediction module; Lt; / RTI >
If it is difficult to input the correct item in the step (a), the code linking module of the market forecast information providing server associates the industry technical classification code, the standard industry classification code, the item code and the IPC code, And an IPC code of an industrial technology classification code for each item code and stores it as an original sheet by using a spreadsheet program,
Accessing a market prediction information providing server from the user terminal, selecting an association menu, inputting a code or a name,
The code linking module comprising only the necessary data corresponding to the inputted code or name of the data of the original sheet and storing the data in the first preparation procedure sheet,
Summing the score (degree of association) related to the code linkage based on the result obtained in the first preparation procedure sheet and storing the sum in the second preparation procedure sheet;
Arranging the result obtained in the second preparation procedure sheet and storing the result sheet as a result sheet,
Standardizing the degree of association between the codes on a 100-point basis and providing the same to a user terminal in a search result display field of a search sheet;
Performing a step of selecting an item corresponding to the code output to the user terminal,
In the step (b), the data classified by the industry classified by collecting and collecting the middle class or small class industry data (production amount or sales amount) corresponding to the input item is standardized on the basis of the initial year value and the relative value graph The market size growth characteristics of the industry are identified and the standard value of the subclass is divided by the standard value of the corresponding upper class to calculate the relative size of each item to the upper class and the type is classified again at each industrial classification level according to the given type classification standard In addition,
The cumulative sales amount Y _t up to immediately before the present time (t) can be obtained by using a logistic growth model curve represented by the following equation,

)to be.
Wherein the cumulative observation value Y _t up to immediately before the current point (t) is derived using a bath growth model curve represented by the following equation.

The method according to claim 1,
Wherein the past production amount (market size, shipment amount) is further input in the step (a).

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The method according to claim 1,
Collecting data including a long-term and short-term related index for each business type of the corresponding item in the market attractiveness calculating module of the market forecast information providing server in the step (c)
Reflecting the specific characteristics of each industry and industry by analyzing the major variables by sector,
And estimating the market attractiveness by quantitatively calculating the market attractiveness by reflecting the short-term and long-term evaluations of the industry.

The method according to claim 6,
Wherein the major variables of the industry are the production value of the manufacturing industry and the main variable of the service industry is the sales value, wherein the employment increase rate, the wage change rate, the price change rate, and the rate change rate are added.

The method according to claim 6,
Calculating the standardized growth forecast by time using the rate of change of the main variables at the step of quantitatively calculating the market attractiveness,
The growth forecast

Standardize from 0 to 100 using the formula,
Using the standardized growth estimates,

We quantify long-term market attractiveness index by formula,

Wherein the short-term market attractiveness index is quantified by a formula.
Where F is the standardized growth forecast for each time point, V is the standardized value of F as an index of 0 to 100, ω is the time weighted value for each long term, and 1, 3, , 4th and 6th quarters.

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9. The method of claim 8,

Through the formula, we derive the relative level of market attractiveness relative to the industry (manufacturing or service industry) to which each industry (j) belongs, and evaluate it based on the growth rate of the relevant industry (manufacturing or service industry) And quantifying the market forecast information.

9. The method of claim 8,
After quantitatively calculating the above market attractiveness, short and long term growth types of each industry (industry) are classified into four types (PN, PP, NP, NN) by using the standardized growth forecast and market attractiveness, And evaluating the market forecast information.

The method according to claim 1,
The KPI (Key Performance Index) of a competitive factor is classified into a product (service) and a product (service) by classifying the market attractiveness into a competition factor and an environmental factor by connecting the market prediction information providing server in the user terminal, The KPIs of environmental factors are classified into A (highest grade) - E grade (lowest grade) and classified qualitatively into competitive condition and regulatory factor.
The market attractiveness calculating module of the market forecast information providing server divides the market attractiveness into the competitive factors and the environmental factors in the step (c), and the competitive factors are matrixed into the product (service) awareness and the market entry ease, Is matched to a competitive situation and a regulatory factor, and then positive (+) is added to calculate the qualitative index of the overall market attractiveness.

A computer program stored in a medium coupled with the hardware to provide market forecast information of the item,
(a) receiving input of market forecast information from a user terminal, a saturation market scale, a market cycle, a current market point among market cycles, and a market attractiveness (qualitative)
(b) typing an overall growth model model of the item based on the input data;
(c) combining trends of long-term time-series data based on the above-described items with trends based on recent time-series data, and applying the trend-based data trends to the growth model to derive a forecast curve,
(d) providing the user terminal with market prediction information including a market forecast (growth) curve of the item and a market scale,
If it is difficult to input the correct item in the step (a), the code link module of the market forecast information providing server associates the industry technical classification code, the standard industry classification code, the item code and the IPC code, In the state that the industrial technical classification code and the IPC code are associated with each item code and stored in the original sheet by using the spreadsheet program,
Accessing a market prediction information providing server from the user terminal, selecting an association menu, inputting a code or a name,
The code linking module comprising only the necessary data corresponding to the inputted code or name of the data of the original sheet and storing the data in the first preparation procedure sheet,
Summing the score (degree of association) related to the code linkage based on the result obtained in the first preparation procedure sheet and storing the sum in the second preparation procedure sheet;
Arranging the result obtained in the second preparation procedure sheet and storing the result sheet as a result sheet,
Standardizing the degree of association between the codes on a 100-point basis and providing the same to a user terminal in a search result display field of a search sheet;
Performing a step of selecting an item corresponding to the code output to the user terminal,
In the step (b), the data classified by the industry classified by collecting and collecting the middle class or small class industry data (production amount or sales amount) corresponding to the input item is standardized on the basis of the initial year value and the relative value graph The market size growth characteristics of the industry are identified and the standard value of the subclass is divided by the standard value of the corresponding upper class to calculate the relative size of each item to the upper class and the type is classified again at each industrial classification level according to the given type classification standard In addition,
The cumulative sales amount Y _t up to immediately before the present time (t) can be obtained by using a logistic growth model curve represented by the following equation,

)to be.
Wherein cumulative observations Y _t up to immediately before the present time (t) are derived using a bath growth model curve represented by the following equation: &_lt; EMI ID = 1.0 >